A Risk Score for Polycystic Ovary Syndrome Based on Meta-Analysis and Machine Learning of Gut Microbiota Signatures.

Abstract

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine and metabolic disorder among reproductive-age women, in which emerging evidence suggests a substantial role played by the gut microbiota. To comprehensively evaluate gut microbiota alterations in PCOS and identify microbial biomarkers through integrated analysis, a systematic search of PubMed, Web of Science, and Embase was conducted for studies employing 16S rRNA gene sequencing of fecal samples from PCOS cohorts. Ten eligible PCOS cohorts, comprising 858 individuals, were included in the study, from which a risk score was derived using a 20-gene gut microbial signature associated with PCOS. Meta-analysis at the genus level identified that Subdoligranulum, NK4A214_group, and Collinsella significantly decreased, and Bacteroides increased in PCOS across multiple cohorts. Machine learning analysis identified a 20-genus microbial signature using the least absolute shrinkage and selection operator (LASSO) method, which was used to construct a risk score with an AUC of 0.835 in diagnosis prediction. Network analysis further identified Negativibacillus and Lachnospiraceae_UCG_010 as potential driver microbes in PCOS. The analysis in this study highlights key alterations in the gut microbiota across PCOS cohorts. The identified gut microbial signature and derived LASSO-based risk model offer novel insights and a potential tool for PCOS diagnosis.